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==Course Readings==
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| Microbiom || [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193652 Rimoldi_etal_2018_PlosOne] || [https://doi.org/10.1371/journal.pone.0193652.s004 S1 Dataset] || Example
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Revision as of 19:55, 21 June 2019

Biomedical Genomics
July 8-19, 2019
Instructor: Weigang Qiu, Ph.D.
Professor, Department of Biological Sciences, City University of New York, Hunter College & Graduate Center
Adjunct Faculty, Department of Physiology and Biophysics Institute for Computational Biomedicine, Weil Cornell Medical College
Office: B402 Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA
Email: weigang@genectr.hunter.cuny.edu
Lab Website: http://diverge.hunter.cuny.edu/labwiki/

Figure 1. Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)

Course Overview

Welcome to BioMedical Genomics, a computer workshop for advanced undergraduates and graduate students. A genome is the total genetic content of an organism. Driven by breakthroughs such as the decoding of the first human genome and next-generation DNA -sequencing technologies, biomedical sciences are undergoing a rapid and profound transformation into a highly data-intensive field.

Genome information is revolutionizing virtually all aspects of life sciences including basic basic, medicine, and agriculture. Meanwhile, use of genomic data requires life scientists to be familiar with concepts and skills in biology, computer science, as well as data analysis.

This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises.

The pre-requisites of the course includes college-level courses in molecular biology, cell biology, and genetics. Introductory courses in computer programming and statistics are preferred but not strictly required.

Learning goals

By the end of this course successful students will be able to:

  • Describe next-generation sequencing (NGS) technologies & contrast it with traditional Sanger sequencing
  • Explain applications of NGS technology including pathogen genomics, cancer genomics, human genomic variation, transcriptomics, meta-genomics, epi-genomics, and microbiome, and single-cell genomics
  • Visualize and explore genomics data using RStudio
  • Replicate key results using a data set associated with a primary research paper

Useful links

  • Install R and R Studio
  • Unix Tutorial
  • Textbook

Quizzes and Exams

Student performance will be evaluated by attendance, three (4) quizzes and a final report:

  • Attendance: 50 pts
  • Quizzes: 4 x 25 pts = 100 pts
  • Final report: 50 pts

Total: 200 pts

Course Schedule

  • July 8 (Mon), 8:40-12:10
  • July 9 (Tu), 8:40-12:10
  • July 10 (Wed), 8:40-12:10
  • July 11 (Thur), 8:40-12:10
  • July 12 (Fri), 8:40-12:10
  • July 15 (Mon), 8:00-12:10
  • July 16 (Tu), 8:00-12:10
  • July 17 (Wed), 8:00-12:10
  • July 18 (Thur), 8:00-12:10
  • July 19 (Fri), 8:00-12:10

Course Readings

Application Paper link Data set Header text
Microbiom Rimoldi_etal_2018_PlosOne S1 Dataset Example
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example
Example Example Example Example